The Bencao (Herbal) Database（本草数据库）

  1. OmicsAtlas
  2. TranscriptomeAtlas
  3. miRNA Atlas
  4. miR27
  5. kga-miR27b

Accession: B38641523

Entry

ID: kga-miR27b
Alias: F13891.L000081.A25*
Sequence: UUCACAGUGGCUAAGUUCUGA
Description: microRNA mir-27 predicted
Species: 山柰  Species latin name: Kaempferia galanga
TCM Name: Shannai(山柰)  
TCM Resource (Sample number): 山柰(1)
Predicted by Mirnovo: -
Evidence support: Infernal
Experimental status: Not yet

Homologs

miRBase Homologs

None

Bencao Homologs

Bjl-miR27h; asc-miR27e; bca-miR27h; bch-miR27h; bpi-miR27e; cci-miR27d; cfe-miR27i; cit-miR27bi; cmd-miR27ao; cof-miR27d; epr-miR27c; gel-miR27c; hcr-miR27ao; kga-miR27b; ljp-miR27d; llu-miR27d; nte-miR27b; poe-miR27ao; ppr-miR27e; psu-miR27c; sch-miR27d; sdf-miR27c; sor-miR27be; vph-miR27d; zof-miR27f;

Rfam Alignments

>> mir-27  microRNA mir-27
 rank     E-value  score  bias mdl mdl from   mdl to       seq from      seq to       acc trunc   gc
 ----   --------- ------ ----- --- -------- --------    ----------- -----------      ---- ----- ----
  (1) !    0.0014   20.4   0.0  cm       56       75 ~.           1          20 + ~. 1.00    5' 0.45
                                                              ?????  ???????????   NC
                                                        ~~~~~~>>>>>-->>>>>>>>>>>:: CS
                                              mir-27 15 <[41]*uUCACAGuGGCUAAGuuCuG 75
                                                              UUCACAGUGGCUAAGUUCUG
                                          zof-miR27f  1 <[ 0]*UUCACAGUGGCUAAGUUCUG 20
                                                        ......******************** PP

Targets

Predicted by TargetFinder

NM_001079524 ;   NM_001079525 ;   NM_001128431 ;   NM_001131019 ;   NM_001134337 ;   NM_001134338 ;   NM_001135153 ;   NM_001242376 ;   NM_001318751 ;   NM_001318752 ;   NM_001318753 ;   NM_001318754 ;   NM_001321749 ;   NM_001351655 ;   NM_001351657 ;   NM_001351658 ;   NM_001351659 ;   NM_001351660 ;   NM_001363846 ;   NM_001365999 ;   NM_001367971 ;   NM_001367972 ;   NM_002055 ;   NM_003566 ;   NM_006452 ;   NM_007219 ;   NM_015104 ;   NM_015284 ;   NM_015359 ;   NM_021916 ;   NM_030820 ;   NR_104664 ;   NR_134849 ;   NR_134850 ;   NR_134851 ;  

Predicted by TAPIR

NM_001005291 ;   NM_001079524 ;   NM_001079525 ;   NM_001128431 ;   NM_001131019 ;   NM_001134337 ;   NM_001134338 ;   NM_001159588 ;   NM_001166588 ;   NM_001242376 ;   NM_001261418 ;   NM_001318751 ;   NM_001318752 ;   NM_001318753 ;   NM_001318754 ;   NM_001321096 ;   NM_001321749 ;   NM_001351655 ;   NM_001351656 ;   NM_001351657 ;   NM_001351658 ;   NM_001351659 ;   NM_001351660 ;   NM_001363846 ;   NM_001367971 ;   NM_001367972 ;   NM_002055 ;   NM_004176 ;   NM_006452 ;   NM_007219 ;   NM_015359 ;   NM_020365 ;   NM_020423 ;   NM_021916 ;   NM_030820 ;   NR_134849 ;   NR_134850 ;   NR_134851 ;